text: # coding: utf-8
  
  """
      Determined API (Beta)
  
      Determined helps deep learning teams train models more quickly, easily share GPU resources, and effectively collaborate. Determined allows deep learning engineers to focus on building and training models at scale, without needing to worry about DevOps or writing custom code for common tasks like fault tolerance or experiment tracking.  You can think of Determined as a platform that bridges the gap between tools like TensorFlow and PyTorch --- which work great for a single researcher with a single GPU --- to the challenges that arise when doing deep learning at scale, as teams, clusters, and data sets all increase in size.  # noqa: E501
  
      OpenAPI spec version: 0.1
      Contact: community@determined.ai
      Generated by: https://github.com/swagger-api/swagger-codegen.git
  """
  
  
  import pprint
  import re  # noqa: F401
  
  import six
  
  
  class V1ReportTrialTrainingMetricsResponse(object):
      """NOTE: This class is auto generated by the swagger code generator program.
  
      Do not edit the class manually.
      """
  
      """
      Attributes:
        swagger_types (dict): The key is attribute name
                              and the value is attribute type.
        attribute_map (dict): The key is attribute name
                              and the value is json key in definition.
      """
      swagger_types = {
      }
  
      attribute_map = {
      }
  
      def __init__(self):  # noqa: E501
          """V1ReportTrialTrainingMetricsResponse - a model defined in Swagger"""  # noqa: E501
          self.discriminator = None
  
      def to_dict(self):
          """Returns the model properties as a dict"""
          result = {}
  
          for attr, _ in six.iteritems(self.swagger_types):
              value = getattr(self, attr)
              if isinstance(value, list):
                  result[attr] = list(map(
                      lambda x: x.to_dict() if hasattr(x, "to_dict") else x,
                      value
                  ))
              elif hasattr(value, "to_dict"):
                  result[attr] = value.to_dict()
              elif isinstance(value, dict):
                  result[attr] = dict(map(
                      lambda item: (item[0], item[1].to_dict())
                      if hasattr(item[1], "to_dict") else item,
                      value.items()
                  ))
              else:
                  result[attr] = value
          if issubclass(V1ReportTrialTrainingMetricsResponse, dict):
              for key, value in self.items():
                  result[key] = value
  
          return result
  
      def to_str(self):
          """Returns the string representation of the model"""
          return pprint.pformat(self.to_dict())
  
      def __repr__(self):
          """For `print` and `pprint`"""
          return self.to_str()
  
      def __eq__(self, other):
          """Returns true if both objects are equal"""
          if not isinstance(other, V1ReportTrialTrainingMetricsResponse):
              return False
  
          return self.__dict__ == other.__dict__
  
      def __ne__(self, other):
          """Returns true if both objects are not equal"""
          return not self == other
  